Scaling of Workload Traces

Abstract

The design and evaluation of job scheduling strategies often require simulations with workload data or models. Usually workload traces are the most realistic data source as they include all explicit and implicit job patterns which are not always considered in a model. In this paper, a method is presented to enlarge and/or duplicate jobs in a given workload. This allows the scaling of workloads for later use on parallel machine configurations with a different number of processors. As quality criteria the scheduling results by common algorithms have been examined. The results show high sensitivity of schedule attributes to modifications of the workload. To this end, different strategies of scaling number of job copies and/or job size have been examined. The best results had been achieved by adjusting the scaling factors to be higher than the precise relation between the new scaled machine size and the original source configuration.